A FAULT DIAGNOSIS METHOD BASED ON IMPROVED ARTIFICAL BEE COLONY OPTIMIZE SUPPORT VECTOR MACHINE
Aiming at the fact that the fault diagnosis performance of support vector machine（ SVM） highly depends on the parameters selection,a fault diagnosis method based on improved artificial bee colony（ IABC） optimize SVM was proposed. In order to improve search ability of ABC,Levy flight strategy was int...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Journal of Mechanical Strength
2018-01-01
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| Series: | Jixie qiangdu |
| Subjects: | |
| Online Access: | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2018.02.006 |
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| _version_ | 1841535491003908096 |
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| author | WU YinHua XU QiongYan |
| author_facet | WU YinHua XU QiongYan |
| author_sort | WU YinHua |
| collection | DOAJ |
| description | Aiming at the fact that the fault diagnosis performance of support vector machine（ SVM） highly depends on the parameters selection,a fault diagnosis method based on improved artificial bee colony（ IABC） optimize SVM was proposed. In order to improve search ability of ABC,Levy flight strategy was introduced and improved the original ABC algorithm. Use the IABC to optimize SVM parameters can effectively improve the classification performance of SVM. Different fault type and different fault degree of rolling bearing fault diagnosis experiment results show that the IABC can obtain better parameters when compared with ABC,GA and PSO,improved the fault diagnosis accuracy of SVM and can applied to fault diagnosis efficiently |
| format | Article |
| id | doaj-art-d9a8d7a1cef34272ac95f36efac2605d |
| institution | Kabale University |
| issn | 1001-9669 |
| language | zho |
| publishDate | 2018-01-01 |
| publisher | Editorial Office of Journal of Mechanical Strength |
| record_format | Article |
| series | Jixie qiangdu |
| spelling | doaj-art-d9a8d7a1cef34272ac95f36efac2605d2025-01-15T02:32:32ZzhoEditorial Office of Journal of Mechanical StrengthJixie qiangdu1001-96692018-01-014028729230601428A FAULT DIAGNOSIS METHOD BASED ON IMPROVED ARTIFICAL BEE COLONY OPTIMIZE SUPPORT VECTOR MACHINEWU YinHuaXU QiongYanAiming at the fact that the fault diagnosis performance of support vector machine（ SVM） highly depends on the parameters selection,a fault diagnosis method based on improved artificial bee colony（ IABC） optimize SVM was proposed. In order to improve search ability of ABC,Levy flight strategy was introduced and improved the original ABC algorithm. Use the IABC to optimize SVM parameters can effectively improve the classification performance of SVM. Different fault type and different fault degree of rolling bearing fault diagnosis experiment results show that the IABC can obtain better parameters when compared with ABC,GA and PSO,improved the fault diagnosis accuracy of SVM and can applied to fault diagnosis efficientlyhttp://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2018.02.006Artificial bee colonyLevy flightSupport vector machineParameters optimizationFault diagnosis |
| spellingShingle | WU YinHua XU QiongYan A FAULT DIAGNOSIS METHOD BASED ON IMPROVED ARTIFICAL BEE COLONY OPTIMIZE SUPPORT VECTOR MACHINE Jixie qiangdu Artificial bee colony Levy flight Support vector machine Parameters optimization Fault diagnosis |
| title | A FAULT DIAGNOSIS METHOD BASED ON IMPROVED ARTIFICAL BEE COLONY OPTIMIZE SUPPORT VECTOR MACHINE |
| title_full | A FAULT DIAGNOSIS METHOD BASED ON IMPROVED ARTIFICAL BEE COLONY OPTIMIZE SUPPORT VECTOR MACHINE |
| title_fullStr | A FAULT DIAGNOSIS METHOD BASED ON IMPROVED ARTIFICAL BEE COLONY OPTIMIZE SUPPORT VECTOR MACHINE |
| title_full_unstemmed | A FAULT DIAGNOSIS METHOD BASED ON IMPROVED ARTIFICAL BEE COLONY OPTIMIZE SUPPORT VECTOR MACHINE |
| title_short | A FAULT DIAGNOSIS METHOD BASED ON IMPROVED ARTIFICAL BEE COLONY OPTIMIZE SUPPORT VECTOR MACHINE |
| title_sort | fault diagnosis method based on improved artifical bee colony optimize support vector machine |
| topic | Artificial bee colony Levy flight Support vector machine Parameters optimization Fault diagnosis |
| url | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2018.02.006 |
| work_keys_str_mv | AT wuyinhua afaultdiagnosismethodbasedonimprovedartificalbeecolonyoptimizesupportvectormachine AT xuqiongyan afaultdiagnosismethodbasedonimprovedartificalbeecolonyoptimizesupportvectormachine AT wuyinhua faultdiagnosismethodbasedonimprovedartificalbeecolonyoptimizesupportvectormachine AT xuqiongyan faultdiagnosismethodbasedonimprovedartificalbeecolonyoptimizesupportvectormachine |